Vipul Singhal

Vipul Singhal

I am interested in developing AI and ML algorithms for spatial multi-omics data analysis.

Publications

You can find the full list of my articles on my Google Scholar profile.

BANKSY unifies cell typing and tissue domain segmentation for scalable spatial omics data analysis

Published in Nature Genetics, 2024

BANKSY is an algorithm with R and Python implementations that identifies both cell types and tissue domains from spatially-resolved -omics data by incorporating spatial kernels capturing microenvironmental information, applicable to a range of spatially-resolved technologies, and scalable to millions of cells.

Recommended citation: Singhal, V., Chou, N., Lee, J. et al. (2024). "BANKSY unifies cell typing and tissue domain segmentation for scalable spatial omics data analysis." Nat Genet. https://www.nature.com/articles/s41588-024-01664-3

DUBStepR is a scalable correlation-based feature selection method for accurately clustering single-cell data

Published in Nature Communications, 2021

DUBStepR is a feature selection method that relies on the intuition of finding the most informative features given the set of features already identified.

Recommended citation: Ranjan, B. et al. "DUBStepR is a scalable correlation-based feature selection method for accurately clustering single-cell data." Nat. Commun.. 12, 5849 (2021). https://doi.org/10.1038/s41467-021-26085-2

A MATLAB toolbox for modeling genetic circuits in cell-free systems

Published in OUP Synthetic Biology, 2021

Txtlsim is a toolbox for simulating cell free reactions using mass action kinetics I this paper, we show how models of subsystems of a circuit can be individually characterized, and composed into the full system, whose behavior can be accurately predicted.

Recommended citation: Singhal et al. "A MATLAB toolbox for modeling genetic circuits in cell-free systems." Synthetic Biology. Volume 6, Issue 1, 2021, ysab007. https://doi.org/10.1093/synbio/ysab007

Transforming Data Across Environments Despite Structural Non-Identifiability

Published in American Control Conference, 2019

We present a framework for batch correcting genetic circuit data across environments, and show that parameter structural non-identifiability need not hinder this goal. We give parameter consistency conditions under which we can perform such correction despite the parameters not being identifiable.

Recommended citation: V. Singhal and R. M. Murray (2019). "Transforming Data Across Environments Despite Structural Non-Identifiability." American Control Conference (ACC), Philadelphia, PA, USA, 2019 ,pp. 5639-5646, doi: 10.23919/ACC.2019.8814953.